{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "from statsmodels.graphics.tsaplots import plot_acf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Set the current working directory\n",
    "os.chdir('D:/Practical Time Series/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read data from Excel file\n",
    "daily_temp = pd.read_excel('datasets/mean-daily-temperature-fisher-river.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean_Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988-01-02</td>\n",
       "      <td>-20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988-01-03</td>\n",
       "      <td>-22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1988-01-04</td>\n",
       "      <td>-30.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>-31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1988-01-06</td>\n",
       "      <td>-27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1988-01-07</td>\n",
       "      <td>-26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1988-01-08</td>\n",
       "      <td>-26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988-01-09</td>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1988-01-10</td>\n",
       "      <td>-23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Mean_Temperature\n",
       "0 1988-01-01            -23.00\n",
       "1 1988-01-02            -20.50\n",
       "2 1988-01-03            -22.00\n",
       "3 1988-01-04            -30.50\n",
       "4 1988-01-05            -31.00\n",
       "5 1988-01-06            -27.50\n",
       "6 1988-01-07            -26.25\n",
       "7 1988-01-08            -26.50\n",
       "8 1988-01-09            -23.00\n",
       "9 1988-01-10            -23.50"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display first 20 rows of the DataFrame\n",
    "daily_temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Make formatted date as the row index of the dataset and drop the Date column\n",
    "daily_temp.index = daily_temp['Date'].map(lambda date: pd.to_datetime(date, '%Y-%m-%d'))\n",
    "daily_temp.drop('Date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean_Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-02</th>\n",
       "      <td>-20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-03</th>\n",
       "      <td>-22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-04</th>\n",
       "      <td>-30.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-05</th>\n",
       "      <td>-31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-06</th>\n",
       "      <td>-27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-07</th>\n",
       "      <td>-26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-08</th>\n",
       "      <td>-26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-09</th>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-10</th>\n",
       "      <td>-23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mean_Temperature\n",
       "Date                        \n",
       "1988-01-01            -23.00\n",
       "1988-01-02            -20.50\n",
       "1988-01-03            -22.00\n",
       "1988-01-04            -30.50\n",
       "1988-01-05            -31.00\n",
       "1988-01-06            -27.50\n",
       "1988-01-07            -26.25\n",
       "1988-01-08            -26.50\n",
       "1988-01-09            -23.00\n",
       "1988-01-10            -23.50"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Re-display the first 10 rows of the modified DataFrame\n",
    "daily_temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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pdi8nuFx114DTfvOLb972sjJ45Yf/fQP/e/W9CFoUL+Q1c1A0in/79ldr1ZxASAUwAlAQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13720c180b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('Daily temperatures of Fisher River, TX, US')\n",
    "daily_temp.plot(ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_08.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of monthly mean temperature dataset: (48,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1988-01-31   -22.137097\n",
       "1988-02-29   -19.025862\n",
       "1988-03-31    -8.258065\n",
       "1988-04-30     2.641667\n",
       "1988-05-31    11.290323\n",
       "1988-06-30    19.291667\n",
       "1988-07-31    19.048387\n",
       "1988-08-31    17.379032\n",
       "1988-09-30    10.675000\n",
       "1988-10-31     2.467742\n",
       "Freq: M, Name: Mean_Temperature, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate monthly mean temperature\n",
    "montly_resample = daily_temp['Mean_Temperature'].resample('M')\n",
    "monthly_mean_temp = montly_resample.mean()\n",
    "print('Shape of monthly mean temperature dataset:', monthly_mean_temp.shape)\n",
    "monthly_mean_temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0J/e4yHbnrZ3T6aIugrLPhl3RZlN57BoOalLq3g2zizEcJ8DJXAXVRlOXABxn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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137210feb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the monthly mean temparature\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('Monthly mean temperatures of Fisher River, TX, US')\n",
    "monthly_mean_temp.plot(ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_09.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yAXYfGKR/YIiJbLiBTWQL9A8MsfvAINdvX1erxYi0tGKgj0S19OL3TRrTYrXBbwAGSu4f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\nAl5EJKYU8CIiMaWAFxGJqf8LS/Hbx4WS2ckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137211c5ac8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ajgKiuekXdPUU8NJyFBDNT7+gq6OAl5akgJBWoF40IiIxpYAXEYkpBbyISEwp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\nAl5EJKYU8CIiMaWAFxGJqf8LS/Hbx4WS2ckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137211c5ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ACF of the monthly mean temparature using pandas.tools.plotting.autocorrelation_plot\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('ACF of monthly mean temperatures of Fisher River, TX, US')\n",
    "#autocorrelation_plot(monthly_mean_temp, ax=ax)\n",
    "plot_acf(monthly_mean_temp,lags=30,\n",
    "         title='ACF of monthly mean temperatures of Fisher River, TX, US',\n",
    "         ax=ax\n",
    "        )\n",
    "#plt.savefig('plots/ch2/B07887_02_10.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sk5x9JgPLfofH9xwjmy3v5hupjorUYbj7fQRBITrtpsiwA+8vNa0Ecm0DEpGg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\nSEn+Px2vfgbaWFE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1372105a588>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sk5x9JgPLfofH9xwjmy3v5hupjorUYbj7fQRBITrtpsiwA+8vNa0Ecm0DEpGg\nobYBMtGpqHXiUkvvOpZrG9DaFMNAbQOkIaiodeKaMHdJTUaxmPHJS1bpCarSUIYraj132awa5UpK\noYBR52Ix49xls/RDkoahotaJS0VSIlJVKmqduHSFISJVpaLWiUsBQ0SqTkWtE5OKpEREpCQKGCIi\nUhIFDBERKYkChoiIlEQBQ0RESqKAISIiJVHAEBGRkihgiIhISRQwRESkJAoYIiITQDbrDJ72croX\nv4aHdhwkU4NOp8oKGGZ2mpk9YGbPhO+ntPM3syVm9mMz225m28zsQ5F515nZPjPbGr7Wl5MfEZFG\nlOt0qvust9F9+mv5wB3/yRU3/6LqQaPcK4xrgYfcfQXwUDieLw181N1XA68G3m9mqyPz/9Hd14Qv\n9bwnIpIn1+mUx4NOp/qTGbZ2drN5Z1dV81FuwNgAfCMc/gZwaf4C7n7A3R8Ph08Q9N29uMztiohM\nGoU6nRpIZti+v6eq+Sg3YMx39wPh8IvA/OEWNrPlwKuAX0Qmf8DMnjCzWwoVaUXSbjSzLWa25dCh\nQ2VmW0Rk4sh1OhXV1hJn9aIZVc3HiAHDzB40s6cKvDZEl3N3B4oWqJnZNOA7wIfdPRcWvwK8DFgD\nHAC+WCy9u29y97Xuvnbu3LkjfzIRkQaR63SqrTnodGpqS5w1SzpYt3JeVfMxYn8Y7n5hsXlmdtDM\nFrr7ATNbCBQsUDOzZoJg8S13/25k3Qcjy3wN+P5oMi8iMhnkOp3qPNbPoRMJVi+awbqV84hXudOp\ncjtQuhe4Erg+fP9e/gJmZsDNwA53/4e8eQsjRVpvB54qMz/jatHMNrr7U6eUJYqIjLdYzHjty+ew\ndPbU2uWhzPTXAxeZ2TPAheE4ZrbIzHJ3PP0OcAXwpgK3z37ezJ40syeANwIfKTM/46qtJc7Zi2bQ\n2qzmKyIy+ZR1heHuR4ALCkzfD6wPh38GFLxucvcrytl+LUxpDoLGjgMnGEhmap0dEZGq0V/lMWht\nCoJGe2u81lkREakaBYwxao7HWL1wBtOnlFsNJCIyMShglKEpHmPVwhnMaFPQEJHGp4BRpnjMWLVg\nBh1Tm2udFSF45k5y9pkMLPsdHt9zjGwNHtAm40/fc20oYFRALGasnD+d2dNaap2VSS33gLbe1Zcy\ncMbrueHhZ/jcD3boZNJg9D3XjgJGhcRixop505g7vbXWWZm0cg9ooyl4QFsinWVXVy9bO7trnTWp\nIH3PtaOIGKrWAAARVUlEQVSAUUFmxpnzpjF/hoJGLRR6QFsynWX3kb4a5UjGg77n2lHAGAcvmzuN\nRR1Tap2NSafQA9pammIsn91eoxzJeND3XDsKGONk2ex2Tp/VVutsTCq5B7S1NgUPaGttinHmvGms\nWdJR66xJBel7rh3dDzqOlpw2lXjM2HOkv9ZZmRRyD2jb2tnN7iN9LJ/dzpolHcSq/IA2GV/6nmtH\nAWOcLepoIx4znj/ch+smjnEXixnnLpvFucuKdq0iDUDfc20oYFTB/BlTaInHONKXoDeR0TOoRGRC\nUsCoklntLcxqD9pppDNZ+pIZehNp+hJpehNpEik9Ml1E6psCRg00xWPMbIsxs+2l1uHJdPZk8OhL\npukdTJPKlFeGlWsNm5k2n8f3HFM5r0gNNcLvUQGjTrQ0xWhpeukqBGAwlaEvkaYvkSGVPfUKpHid\niJPJOtd+50l6z74UYk38r4ef4eXzpvHJS1ZNuINUZKKLtk4n3sQNDz/DmRPw91hWwDCz04A7geXA\nbuAd7n6swHK7gRNABki7+9rRpJ+spjTHmdIcZ/a00ad9aMdBdh48AfEgAA2mszx3uI/OY/288vSZ\n9A6m0ZMURKpjSOt0GNI6fSJV3JfbDuNa4CF3XwE8FI4X80Z3X5MLFmNIL6OwbX/PKZXrg8kMh04k\nOHvRTH57+WmsXjSD02e1MaOtiQn0J0dkwmmU1unlFkltANaFw98ANgMfr2J6KeLsRTNoa4nTHwka\nbS1xVi+aAQS3Jc5saz5Zj5LNOr3JND0DKXoGgroUEamMXOv0RCRoTMTW6eVeYcx39wPh8IvA/CLL\nOfCgmT1mZhvHkF5Gad3KeaxZ0sHUljgGTG2Js2ZJB+tWziu4fCxmzJjSzOmzprJ60Qx+e/ks2lub\naGmK6epDpEyN0jp9xCsMM3sQWFBg1qeiI+7uZlasVPx17r7PzOYBD5jZ0+7+k1GkJww0GwGWLl06\nUrYnvXjMuO2q89m8s4vt+3tYvWgG61bOI17i2d/MiMeMeCzOOUs66Dzaz+He5DjnWqQxNUrr9BED\nhrtfWGyemR00s4XufsDMFgJdRdaxL3zvMrO7gfOAnwAlpQ/TbgI2Aaxdu1bVtSWIx4wLVs3nglXl\nXbhNaY6zYv50FsxMsedIPycGVVwlMlqN0Dq93CKpe4Erw+Erge/lL2Bm7WY2PTcMvBl4qtT0Uj+m\nT2nmFYtnsnLBdNpa4rXOjohUWbkB43rgIjN7BrgwHMfMFpnZfeEy84GfmdmvgF8C/8/dfzhceqlv\np7W3cM7pMzljTjvN8Yl1SS2i7l3Hrqy7pNz9CHBBgen7gfXh8HPAOaNJL/XPzFgwcwpzprWwv3uQ\nA8cH1K5D6l6jNKCrFfWHIWVpisdYOnsqa5Z2MHd6K6bfnNQxde9aHgUMqYjWpjhnzpvGKxfPHPKM\nLJF60igN6GpFAUMqqr21idWLZjC1tUmX+FJ3yu3edbLXfyhgyLhoihnTWptUMS51pZwGdNH6j4Ez\nXs8NDz/D536wY1IFDT2tVsZVrmJ877EBXuwZVK+DUlPlNKBrlAcIlkMBQ8ZdUzzG8jntzJ8xhd1H\n+ujuT9U6SzKJjbUB3XD1H5MlYKhISqqmrSXOqoUzWLVQDf9k4im3/qMRKGBI1XVMDRr+LZ8zlSbV\nb8gE0SgPECyHiqSkJsyMhTPbmDOtlb3HBjio+g2pc43yAMFyKGBITTXHY5wxp535M1rZfbif4wOq\n35D61QgPECyHiqSkLkxtCdpvnLVgOlOadViK1CNdYUhdmdXewvQpTTDvNxicOo/H9xybdJf9IvVK\nAUPqSibrvOuWX3LsrLfhsSZu3LyLsxZM5+MXn6WHG4rUmK79pa5s3tnF1s5uPB48HK4/meHpF09w\nYjDFWQumM3tai7qMFakRXWFIXdm2v4eBZGbItIFkhh0HTnDh6gXMam8hnclytC/Jod4EPQPq/U+k\nWhQwpK6cvWgGbS1x+iNBo60lzupFM06ON8VjzJsxhXkzpjCYynC4N8Hh3uQpgUZEKqusIikzO83M\nHjCzZ8L3U+41M7OVZrY18uoxsw+H864zs32ReevLyY9MfOtWzmPNkg6mtsQxYGpLnDVLOli3cl7B\n5ac0xzl91lTWLOnglafPZOHMKXrYocg4KfcK41rgIXe/3syuDcc/Hl3A3XcCawDMLA7sA+6OLPKP\n7v6FMvMhFZbJOv0dLyPZPp+Hdhxk3cp5xKtQeRCPGbdddT6bd3axfX8PqxfNKHnb01qbmNbaxLLZ\nU0mksyQzWVIn351kJksynSWVyb1Uiy4yGuUGjA3AunD4G8Bm8gJGnguAZ919T5nblXGUyTpX3PwL\nDq14Kx5r4gN3/CdrlnRw21XnVy1oXLBqPhesmj+m9GbGlOY4U5qHf15VNhsEkdTJQOKkMlnSebdj\n+SiaoGcdEukMyXSWRDqr1uvSUMoNGPPd/UA4/CIw0i/8MuCOvGkfMLN3AVuAj7r7sUIJzWwjsBFg\n6dKlY8+xjGjInUpAfzLD1s5uNu/sGvNJvB7FYsaU2MiBZazcg4CUSGdJpLIk0pkhw8l0VrcKy4Qy\nYsAwsweBBQVmfSo64u5uZkUPfzNrAd4GfCIy+SvA3wIevn8R+JNC6d19E7AJYO3atfqZjaNidypt\n39/TUAFjvJkZrU1xWpviMKXwMtEgMpjKcHwgRW8irSsTqUsjBgx3v7DYPDM7aGYL3f2AmS0EuoZZ\n1SXA4+5+MLLuk8Nm9jXg+6VlW8ZTKXcqSWXkB5QlBH0sdPcnOdafors/qasQqRvlNty7F7gyHL4S\n+N4wy76TvOKoMMjkvB14qsz8SAWM9k4lqayWpuC24ZULprN2+WmsXDCdudNbdfeX1Fy5dRjXA3eZ\n2VXAHuAdAGa2CPi6u68Px9uBi4D35aX/vJmtISiS2l1gvtRAOXcqSWXFY8Zp7S2c1t6Cu3MikeZY\nX3D1oXYnUm1lBQx3P0Jw51P+9P3A+sh4HzC7wHJXlLN9GT/l3KlUq1tyG52ZMWNKMzOmNLNsdlCv\ndLQ/ybG+pOo9pCrU0lsqqta35E4mbS1xFre0sbijjWQ6y7H+JEd6k/QMphQ8ZFzo4YNSUYUeHpi7\nJVfGT0tTjPkzprB60Qx+a9ksXj63nY6pzZhitFSQAoZU1HC35Ep1NIfP2lq1MAwe89qZ1a7gIeVT\nkZRUlG7JrS/N8Rjzpk9h3vQpwVN++5Mc7UtyvD+l23Vl1BQwpKJyt+Ru7exmIJmhTbfk1o2mvOBx\nrD/F0b6k2npIyRQwpKJ0S+7E0BSPMXd6K3Ont5LJOicGgxbmfYkMvYk0yXS21lmUOqSAIRVX7sMD\npbriMaNjagsdU1tOTkums/Ql0kEQSabpS6RJpnUZMtkpYIjIKVqaYrQ0tTCr/aUgkkhn6EtkXgok\nibQeET/JKGCISElyz706LRJEBlMZBlMZ0lknm3Uy7mSyTjbLS8Phe3Q4eIesu9qMTCAKGCIyZqX0\nOzJWxfohiU5OZ510NuggK5XNko70aZLORPs4CYYVnMqjgCEidcmKNByJTm6JGS3EoKXgoqdIh8Ek\nlckymMrSn0zTn8zQn1QdTSkUMERk0miKx2iKB1dG06cAtJ6cl8pk6U9k6E8Fd4sNhIFEtxy/RAFD\nRISgkePMqTFm0nxymrszmMrSl0wzkMzQF16RJFKT87ZjBQwRkSLMjLaWOG0tQ+tpUpngtuO+5Et3\njU2GIKKAISIySs3xWNh25aVpuSDSyA0gFTBERCrgpSDSuA0gywoYZvaHwHXAKuA8d99SZLmLgX8C\n4gQ98V0fTj8NuBNYTtDj3jvc/Vg5eRIRqReFGkAm01kS6QyJdDYcDsdTWZKZ4NbgelXuFcZTwO8D\nXy22gJnFgRsJumjdCzxqZve6+3bgWuAhd7/ezK4Nxz9eZp5EROpWEERiTC8yP53JBZHskOCSSGWJ\n17hf93K7aN0Bxe+XDp0H7HL358Jlvw1sALaH7+vC5b4BbEYBQ0QmseDW3xjtrSMvW23V6EBpMdAZ\nGd8bTgOY7+4HwuEXgaJPqzOzjWa2xcy2HDp0aHxyKiIiRY0YMMzsQTN7qsBrQyUz4sFzAIoW3rn7\nJndf6+5r586dW8lNi4hICUYsknL3C8vcxj5gSWT89HAawEEzW+juB8xsIaCOn0VE6lQ1iqQeBVaY\n2Rlm1gJcBtwbzrsXuDIcvhL4XhXyIyIiY1BWwDCzt5vZXuA1wP8zs/vD6YvM7D4Ad08D1wD3AzuA\nu9x9W7iK64GLzOwZ4MJwXERE6pAVe4RwPVu7dq1v2VKwyYeIiBRhZo+5+9qxpq9GkZSIiDQABQwR\nESmJAoaIiJRkQtZhmNkhYM8Yk88BDlcwO5WifI2O8jU6ytfo1Gu+oLy8LXP3MTdkm5ABoxxmtqWc\nSp/xonyNjvI1OsrX6NRrvqC2eVORlIiIlEQBQ0RESjIZA8amWmegCOVrdJSv0VG+Rqde8wU1zNuk\nq8MQEZGxmYxXGCIiMgYKGCIiUpKGDRhmdrGZ7TSzXWH3r/nzzcxuCOc/YWbnViFPS8zsx2a23cy2\nmdmHCiyzzsyOm9nW8PXp8c5XuN3dZvZkuM1THtRVo/21MrIftppZj5l9OG+ZquwvM7vFzLrM7KnI\ntNPM7AEzeyZ8n1Uk7bDH4jjk6+/N7Onwe7rbzDqKpB32Ox+HfF1nZvsi39X6Immrvb/ujORpt5lt\nLZJ2PPdXwXNDPRxjQ7h7w72AOPAs8DKgBfgVsDpvmfXADwADXg38ogr5WgicGw5PB35dIF/rgO/X\nYJ/tBuYMM7/q+6vAd/oiQcOjqu8v4A3AucBTkWmfB64Nh68F/m4sx+I45OvNQFM4/HeF8lXKdz4O\n+boO+MsSvueq7q+8+V8EPl2D/VXw3FAPx1j01ahXGCf7EXf3JJDrRzxqA/BNDzwCdISdOI0bdz/g\n7o+HwycIHve+ePhUdaPq+yvPBcCz7j7WFv5lcfefAEfzJm8g6Iue8P3SAklLORYrmi93/5EH3QoA\nPELQaVlVFdlfpaj6/soxMwPeAdxRqe2VaphzQ82PsahGDRjD9SM+mmXGjZktB14F/KLA7NeGxQk/\nMLOzq5QlBx40s8fMbGOB+TXdXwQdbxX7Iddif0FpfdLXer/9CcGVYSEjfefj4QPhd3VLkeKVWu6v\n1wMH3f2ZIvOrsr/yzg11dYw1asCoa2Y2DfgO8GF378mb/Tiw1N1/E/hfwD1Vytbr3H0NcAnwfjN7\nQ5W2OyILemp8G/C/C8yu1f4awoOygbq6R93MPgWkgW8VWaTa3/lXCIpN1gAHCIp/6sk7Gf7qYtz3\n13Dnhno4xho1YAzXj/holqk4M2smOCC+5e7fzZ/v7j3u3hsO3wc0m9mc8c6Xu+8L37uAuwkuc6Nq\nsr9ClwCPu/vB/Bm12l+hg7liOSveJ32tjrN3A28BLg9PNKco4TuvKHc/6O4Zd88CXyuyvVrtrybg\n94E7iy0z3vuryLmhro6xRg0Yw/UjnnMv8K7w7p9XA8cjl37jIiwjvRnY4e7/UGSZBeFymNl5BN/R\nkXHOV7uZTc8NE1SaPpW3WNX3V0TRf3612F8RpfRJX8qxWFFmdjHwMeBt7t5fZJlSvvNK5yta5/X2\nItur+v4KXQg87e57C80c7/01zLmhvo6x8ahJr4cXwV09vya4e+BT4bSrgavDYQNuDOc/CaytQp5e\nR3BJ+QSwNXytz8vXNcA2gjsdHgFeW4V8vSzc3q/CbdfF/gq3204QAGZGplV9fxEErANAiqCM+Cpg\nNvAQ8AzwIHBauOwi4L7hjsVxztcugjLt3DF2U36+in3n45yv28Jj5wmCE9rCethf4fRbc8dUZNlq\n7q9i54aaH2PRlx4NIiIiJWnUIikREakwBQwRESmJAoaIiJREAUNEREqigCEiIiVRwBARkZIoYIiI\nSEn+Px2vfgbaWFE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1372105a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(monthly_mean_temp,lags=20, title='ACF of monthly mean temperatures of Fisher River, TX, US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10,  -9,  -8,\n",
       "         -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,   5,\n",
       "          6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,  18,\n",
       "         19,  20]),\n",
       " array([-0.24461246, -0.47536716, -0.60369003, -0.55747598, -0.36492553,\n",
       "        -0.06054339,  0.28827097,  0.58481628,  0.72311458,  0.67511451,\n",
       "         0.41732813,  0.03541151, -0.37288728, -0.69382203, -0.82407157,\n",
       "        -0.74793269, -0.46766027, -0.03692674,  0.42650131,  0.80754769,\n",
       "         1.        ,  0.80754769,  0.42650131, -0.03692674, -0.46766027,\n",
       "        -0.74793269, -0.82407157, -0.69382203, -0.37288728,  0.03541151,\n",
       "         0.41732813,  0.67511451,  0.72311458,  0.58481628,  0.28827097,\n",
       "        -0.06054339, -0.36492553, -0.55747598, -0.60369003, -0.47536716,\n",
       "        -0.24461246]),\n",
       " <matplotlib.collections.LineCollection at 0x137212f80b8>,\n",
       " <matplotlib.lines.Line2D at 0x13721305048>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H7aXlWjIYqur6hfqSvJRkY1WdSLIROLnIU90IPFlVLw0892vLST4FfH6ROvYCewGmpqYW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\nSY3/D7Z6s05HaQIbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13720c7ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.acorr(monthly_mean_temp, maxlags=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Take seasonal differences with a period of 12 months on monthly mean temperatures\n",
    "seasonal_diff = monthly_mean_temp.diff(12)\n",
    "seasonal_diff = seasonal_diff[12:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j6WXYrBKXNozX0AL1xRajGW2hw2BGSbsOvbVcCeM2yQaSo9PxgZofJs+H0cUw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\nqZI+z1UsrZJG5PbBktbk9u8kPVKxzi2StuUKl2eqMTY7pYd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1371fa07a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the seasonal differences\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('Seasonal differences')\n",
    "seasonal_diff.plot(ax=ax)\n",
    "plt.savefig('plots/ch2/B07887_02_11.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\avpal\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: 'pandas.tools.plotting.autocorrelation_plot' is deprecated, import 'pandas.plotting.autocorrelation_plot' instead.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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5wtEYz+3u4KqVc/j0NafwuWtP4debj/LZ+7cQmQVFY4m4aZp1k4jsBvYDzwIH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\nzcxyzo3czCzn/g2yhOqgnvqaAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1371fa67f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the seasonal differences\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_title('ACF of Seasonal differences')\n",
    "autocorrelation_plot(seasonal_diff, ax=ax, )\n",
    "plt.savefig('plots/ch2/B07887_02_12.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis is rejected for lags: (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64),)\n"
     ]
    }
   ],
   "source": [
    "#Perform Ljung-Box test on monthly mean temperature to get the p-values\n",
    "#We will use lags of upto 10\n",
    "_, _, _, pval_monthly_mean = stattools.acf(monthly_mean_temp, unbiased=True,\n",
    "                                           nlags=10, qstat=True, alpha=0.05)\n",
    "print('Null hypothesis is rejected for lags:', np.where(pval_monthly_mean<=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis is rejected for lags: (array([], dtype=int64),)\n"
     ]
    }
   ],
   "source": [
    "#Perform Ljung-Box test on monthly mean temperature to get the p-values\n",
    "#We will use lags of upto 10\n",
    "_, _, _, pval_seasonal_diff = stattools.acf(seasonal_diff, unbiased=True,\n",
    "                                            nlags=10, qstat=True, alpha=0.05)\n",
    "print('Null hypothesis is rejected for lags:', np.where(pval_seasonal_diff<=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "acf , confint, qstat , pval_monthly_mean = stattools.acf(monthly_mean_temp, unbiased=True,\n",
    "                                           nlags=10, qstat=True, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  1.        ],\n",
       "       [ 0.53831958,  1.10411245],\n",
       "       [ 0.00433172,  0.87145271],\n",
       "       [-0.51639077,  0.41884459],\n",
       "       [-0.98802073, -0.05197149],\n",
       "       [-1.35543488, -0.33107784],\n",
       "       [-1.56063897, -0.3340289 ],\n",
       "       [-1.53531912, -0.09339225],\n",
       "       [-1.23703902,  0.3452841 ],\n",
       "       [-0.76301287,  0.85903247],\n",
       "       [-0.27877287,  1.3437273 ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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